class_docstr: Maximum likelihood covariance estimator
  
  Read more in the :ref:`User Guide <covariance>`.
  
  Parameters
  ----------
  store_precision : bool
      Specifies if the estimated precision is stored.
  
  assume_centered : bool
      If True, data are not centered before computation.
      Useful when working with data whose mean is almost, but not exactly
      zero.
      If False (default), data are centered before computation.
  
  Attributes
  ----------
  covariance_ : 2D ndarray, shape (n_features, n_features)
      Estimated covariance matrix
  
  precision_ : 2D ndarray, shape (n_features, n_features)
      Estimated pseudo-inverse matrix.
      (stored only if store_precision is True)
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code: def __init__(self, store_precision=True, assume_centered=False):
      self.store_precision = store_precision
      self.assume_centered = assume_centered
  
file_path: catboost/contrib/python/scikit-learn/py2/sklearn/covariance/empirical_covariance_.py
imports: from __future__ import division
  import warnings
  import numpy
  from scipy import linalg
  from ..base import BaseEstimator
  from ..utils import check_array
  from ..utils.extmath import fast_logdet, pinvh
label_desc: Use image node_id 1 to create a new EmpiricalCovariance object from inherited base classes: BaseEstimator with example: obj = EmpiricalCovariance(store_precision, assume_centered)